## The intelligence layer on your cycle

The hardest part of compensation planning isn't entering numbers - it's knowing where to focus. Which employees are at risk? Where are pay decisions drifting from your compensation philosophy? Are managers creating compression problems without realizing it? Pave's smart features work together to surface these signals automatically.

A VP with thousands of employees to review can't give equal attention to every row. Priority sort puts the most critical decisions at the top of the worksheet. AI summaries give instant context on each employee without scanning across columns. Recommendations are pre-populated based on company philosophy, so planners adjust from a baseline rather than starting from scratch. Smart flags fire when a decision creates a problem, and peer comparison is one click away to validate whether the flag is a real issue. For the majority of employees where the recommendation is appropriate and no flags are triggered, the planner reviews and moves on quickly. After the cycle, insights dashboards show whether the aggregate outcomes matched what the company intended.

These features are most powerful when used together. Time is spent on the decisions that matter, not on finding them.


## How these features work together


 ![](https://brainfish-storage-prod.s3-accelerate.amazonaws.com/uploads/eb9365bf-c937-4d2f-b407-a7b5603ae429/6942aebd-f585-4aa2-9026-e4a09a60ef9c/comp_planning_flow.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAUPPN5IL3UTG456X3%2F20260505%2Fus-east-2%2Fs3%2Faws4_request&X-Amz-Date=20260505T195758Z&X-Amz-Expires=3000&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEMH%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMiJGMEQCICdf0yGFcGV9Cgp%2FlTte%2FEI3ueqE%2F2ETFz0OYDxTaP%2FhAiADt2EIu8IHKQU4eDzzpXtkFDcbY300UKjywgQ6b812syrlBQiK%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAIaDDMwODEyNTk3NTI4NyIM%2F9AKpX0d43h9wiEgKrkFsgknz7p%2BvCy2wkYKIUtCSrKjQ1GgRM%2BJS5f%2BQbS7B1FkgqfM6fO0UG12LCQILXxHmkb%2FoWrOZKgq9Ne7p9ZfsrnftsZXck%2F7bV7f0KSq0zWY%2BAzh1Gm4IynZk%2BYmmnTzy2Y5BC7mXGYAHz5AEuVy43oo0SCa9Ewc2hHIl8iwOXRbnAdMbjWatnnSpQyLbvVGwCr0L3KLDmQuk7hzHegBaw4WONlcyZ%2BxBafoF6xzHyJUcKcMYIfR%2FooZcQXgfIbfclHsjrl0bol5aiKkiwupZjAe6KS6vEZnYeToSeaAbABMBhArN8cXcBsURZ%2FHaZ3D2joVHReKU37LGcgDJnZB3euqbDVTdlKeXV%2BgLlOmiiS4LFcUn%2B7vvCfPhBKyQqso2l94SKk2L%2BQtp8MxMNyqvyUTYfLvZzDO3%2BpiPSdQhBrPsIvB%2F3xUstVpZBxsT9ZMNTmkmwvRtdtIcf8B6BE9cs8gAiVdq99nMoLglDFgi0h%2FSpfaYqUerFrXOZw10zC0c3t7PlzVur7yBZSJX%2F9o62smYwC5PQw2UNwyYumrHmVn%2BvHmiuy3jfMW4gMRfFaCoZAJaYMEVvsIP5f38XoYWC5WR8%2FYL7E2V7B0oS%2BdrIGtsAhKL7hJttK5NIhdHDT0tHY28WIA6DwryZ6CZ8tWZ0KfREsd5uem7k6iDjrjYORj92%2ByzTkiN9ubkV1bbbnBnnDEilZiO7Plwfssuvig%2B90K4WAx5EZmpWZ%2BLqkXGpq0spxlSSdJ6RvY0%2F0OMCz43ZZ34b4xm4Q6Cv6u%2F%2BDrCrn1Zc4jhg4irxCPkRwzebcsBci0VVaMpAoK%2BJrcJ9bzqDG%2BLDTGkmhPwq7Ib9mcwOreMz6KaCKK93JDKHYMA0fGHIqa%2FRHq4WarYc%2F4zani8DQq4a1eMfGxR9zXV3iOP6Aav%2B5aTjBBIzCqt%2BjPBjqRAW3tIwBrTjL6XbM7KnP4sI%2FlgQv2UZko3jL1jFgqo9QIU2TILsZtX5m%2B%2BK40KaaoQKgyLj4N2I5DucS0Ms%2FwwvkBJ2ned5wzhnASeNO1%2BdTS88vK23mzksWD9%2BtEU3iqpL4JiTwMsFh%2F5HxP61a5SBaRsHocCeqKnrAiqUbZfk%2FCp7pNpujGIqEG8tWmusK4bYA%3D&X-Amz-Signature=f94fa1439965981a71df24054eb62ecb77bfe81e9ab18fa024b70ed65b54724d&X-Amz-SignedHeaders=host&response-content-disposition=attachment " =859x556")


Each feature is useful on its own, but they compound when used together:

* **Recommendations set the baseline.** Every planner starts from your compensation philosophy rather than guessing. This means the decisions that smart flags catch are genuine exceptions, not noise from inconsistent starting points.
* **Priority sort focuses attention.** Instead of reviewing alphabetically, planners start with the employees who need the most care -- compression risks, high performers below band, promoted employees. Time is spent where it matters.
* **Smart flags catch what recommendations can't.** Recommendations suggest the right amount, but they can't prevent every problem. Flags detect downstream effects like compression, outliers, and equity cliffs that only emerge after planners make their decisions.
* **AI summaries reduce ramp-up time.** When a planner opens an employee -- especially one they don't directly manage -- the summary gives them the full picture in seconds instead of requiring them to scan across columns and piece it together.
* **Peer comparison explains the "why."** When a flag fires or a recommendation seems off, planners can immediately pull up peer data to understand the context. Is this employee truly an outlier, or are their peers all underpaid too?
* **Cycle insights close the loop.** After planners have made their decisions, insights show whether the aggregate outcomes match what you intended. If band adherence didn't improve or high performers didn't get meaningfully different raises, you know what to adjust next cycle.



## [Smart flags](https://support.pave.com/en-us/articles/smart-flags-ktZGxIwjzN)

Smart flags are rule-based indicators that automatically highlight employees matching conditions you define. They surface compensation risks, enforce business rules, and draw planner attention to decisions that need extra scrutiny.

### What you can do with them

* **Surface compensation risks** like manager compression, pay outliers, or equity vesting cliffs
* **Enforce guardrails** by requiring justification (soft stop) or blocking submission entirely (hard stop) when a flag is triggered
* **Guide planner behavior** with custom messaging that explains what the flag means and what the planner should do

### Types of flags

**Pre-built templates** get you started quickly with common compensation scenarios:

| Template | What it detects |
|----|----|
| **Compensation outlier** | An employee's pay differs significantly from their peers, based on a statistical model. |
| **Manager compression** | A manager's compensation is close to or less than one of their direct reports. |
| **Vesting drop** | A significant drop in total vesting equity between two time periods (e.g., equity cliff). |

**Custom flags** let you define any condition using your cycle's columns. Combine multiple rules with AND/OR logic. Common use cases beyond compensation risk:

* Flag employees whose proposed raise is significantly above or below the recommendation
* Flag high performers who are below band minimum
* Flag employees receiving no raise despite strong performance
* Flag raises that exceed a threshold without a promotion
* Flag employees approaching band maximum after their increase

### Compensation controls

Optionally attach a control to any flag to enforce action:

| Control | What it does |
|----|----|
| **Soft stop** | Planner must enter a justification comment before saving. The worksheet can still be submitted. |
| **Hard stop** | Planner cannot save compensation adjustments for the flagged employee until the value is corrected or an authorized user overrides it. |

When you add a control, you also write guidance text that explains to the planner what triggered the flag and what they should do about it.

For detailed configuration instructions, see [Smart Flags](https://support.pave.com/s/article/Smart-Flags). For the pre-built templates, see [Compensation Outliers](https://support.pave.com/s/article/Compensation-Outliers), [Manager Compression](https://support.pave.com/s/article/Manager-Compression-Flag), and [Vesting Drop](https://support.pave.com/s/article/Vesting-Drop-Flag).


## [Priority sort](https://support.pave.com/en-us/articles/priority-sort-MbFxcUDtux)

Priority sort uses AI to reorder the planner's worksheet so the employees who need the most attention appear at the top. Instead of reviewing employees alphabetically or by department, planners start with the decisions that matter most.

### How it works

Admins configure priority factors, each with a weight from 1 to 10. Factors can be based on:

* **Smart flags**: If an employee triggers a specific flag (e.g., compression, outlier), they're weighted as a priority.
* **Column values**: Any worksheet column can be a factor -- promotion status, custom risk indicators, performance ratings, flight risk flags.

Employees matching higher-weighted factors appear first. Planners see a priority indicator on each flagged employee explaining which factors matched and why.

### Why it matters

In a cycle with hundreds or thousands of employees, planners can't give equal attention to every row. Priority sort ensures that compression risks, high performers below band, promoted employees, and other critical cases get reviewed first rather than buried in the middle of the list.


## AI employee summaries

AI summaries give planners instant, contextual briefs about each employee's compensation situation. Instead of scanning across dozens of columns to piece together the story, planners get a generated summary that connects the dots.

### What the summary includes

* Tenure and performance rating
* Current band positioning (compa ratio or range penetration)
* Equity vesting status
* Compensation changes being proposed in this cycle
* Why the employee was flagged as a priority (if applicable)
* Any relevant context from smart flags or manager commentary

### How planners use it

When a planner opens the employee detail panel, the AI summary appears as a brief paragraph with key context. It's especially valuable when a planner is reviewing an employee they don't manage directly (common for senior leaders reviewing rolled-up worksheets) or when a flag fires and the planner needs to quickly understand the full picture.

## [Peer comparison](https://support.pave.com/en-us/articles/setting-up-peer-comparison-QPnw2skmko)

Peer comparison lets planners see how an employee's compensation stacks up against similar employees, directly from the employee detail panel. This gives planners the context to make fair, consistent decisions without needing to manually cross-reference data.

### What planners see

When a planner clicks into an employee's detail view, they can compare that employee against peers matched on dimensions you configure:

* **Compensation range**: Employees in the same band
* **Worksheet columns**: Employees matching on department, level, location, or any custom column

The comparison shows peer averages for key metrics and a scatter plot positioning the employee among their peers, so planners can quickly see if someone is an outlier, lagging behind, or ahead of the group.

### Metrics available

Admins choose which compensation positioning metric planners see by default:

* **Compa ratio**: Position relative to band midpoint (salary or total cash)
* **Range penetration**: Percentage of progression through the band (salary or total cash)


## [Recommendations](https://support.pave.com/en-us/articles/setting-up-recommendation-logic-3JGkuBkByz)

Recommendations anchor every planner decision to your compensation philosophy. They appear as suggested values within input columns, giving planners a starting point based on the rules you've configured.

Recommendations can factor in performance ratings, band positioning, tenure, country, and other dimensions. Each pay type (salary raise, equity refresher, bonus, etc.) can have its own recommendation logic. Planners can follow or override the recommendation for any individual employee.


## Cycle insights

Cycle insights gives you real-time dashboards to monitor the cycle while it's running and analyze outcomes after it closes.

### Cycle progress

Track how many employees have been submitted, where worksheets are in the approval chain, and which planners still have outstanding work. This helps you identify bottlenecks and intervene before deadlines.

### Budget dashboard

Monitor real-time budget consumption. View how much of each budget has been allocated, filter by department or planner, and spot where spending is over or under target before the cycle closes.

### Cycle outcomes

Visualize the impact of the cycle's compensation decisions:

* **Performance distribution**: How ratings are spread across the organization, split by promoted vs. non-promoted
* **Average increase by performance**: Whether higher performers are receiving proportionally larger increases
* **Band adherence**: How the percentage of employees within band changed from pre-cycle to post-cycle, broken down by department
* **Performance vs. compensation positioning**: Heat maps showing the relationship between ratings and compa ratio or range penetration

These views help you validate that the cycle's outcomes align with your compensation philosophy and surface patterns that may need attention in the next cycle.

## Things to know

* **Smart flags are configured per cycle.** Each cycle can have its own set of flags tailored to that cycle's goals.
* **Peer comparison dimensions are set by the admin.** Planners can compare peers, but the available dimensions are controlled in cycle configuration.
* **Cycle insights are available during and after the cycle.** You don't have to wait until finalization to start monitoring outcomes.




Overview of smart features in compensation planning

Configurations overview

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Overview

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Approval chains

Planning windows

Budgets

Recommendation logic

Adding non-integration data to your cycle

Reward letters

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Cycle Progress dashboard

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Worksheet Experience

Peer Comparison

Smart Flags

Priority Sort

Compensation Outliers

Manager Compression Flag

Vesting Drop Flag

Flags and Insights

Compensation Planning

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Integrations 101

Integrations

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Overview of smart features in compensation planning

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Overview of smart features in compensation planning

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